1. Field
The present disclosure is generally related to a method of improving image quality in digital printing or scanning. More specifically, the present disclosure relates to a method of reducing noise in output image data manipulated by segmentation processes such as mixed-raster content (MRC).
2. Description of Related Art
Image data comprises a number of pixels, each pixel corresponding to a defined location in the image. The pixels have a number of components that contribute to defining the image, such as color and intensity. The image data generally includes various color or gray levels, which contribute to the intensity of each pixel in the image. Each pixel of the image is assigned a number representing the amount of light or gray level for that space at that particular spot; i.e., the shade of gray in the pixel. Binary image data has two possible values for each pixel, black (represented by the number “1”) or white (represented by the number “0”). Images that have a large range of shades are referred to as grayscale images. For example, grayscale images have an 8-bit value (or higher) per pixel comprising 256 tones or shades of gray for each pixel in the image (gray level of 0 to 255). Grayscale image data may also be referred to as continuous tone or contone image data. In some instances, it is possible to create the impression of a continuous tone image by using a process such as halftoning, such that the image data is converted and “appears” to be a continuous tone image. The halftone process is generally known, and various methods for halftoning exist.
The intensity of a pixel is expressed within a given range, between a minimum and a maximum (inclusive), from 0 (total presence of color or gray level, i.e., white) and 1 (total absence of color or gray level, black), with any fractional values in between. When outputting image data to an output device (e.g., copier, printer, or multi-function device (MFD)), a percentage scale may be used to identify how much ink is employed for a print job. For example, when printing in halftone, the amount of ink or toner supplied may be between 0% (or none)(i.e., pure white) and 100% (i.e., solid black), inclusive.
Additionally, before image data is output to a digital output device, the image data must be compressed, i.e., coded to minimize the space needed to store and output the image data. In particular, due its large size, digital image data that is output to a device such as a multi-function printer (e.g., for copying and/or printing) demands that images be compressed. For color or grayscale image data that is to be compressed, conversion of such image data to binary image data is generally not sufficient.
Generally, the compression process of image data may be referred to as “lossless” (also referred to as “reversible” or “noiseless”) if the reconstructed image is substantially identical to the original. Alternatively, if the reconstructed image is not identical to the original (i.e., if the reconstructed image is of inferior quality, though not necessarily detected visually), the compression process may be referred to as “lossy” compression. Because lossless compression often does not yield a file size that is small enough for output devices or systems (e.g., copy and print systems), lossy compression is typically used.
One known popular technique for manipulating and compressing digital image data using lossy compression is a standard known as JPEG image format. However, when images are compressed using JPEG format to a smaller file size, the quality of the image data can be severely degraded. For example, color or grayscale image data may be compressed using JPEG compression techniques; however, this type of compression usually produces noise (or fringe) around or near the color parts of an output image. Additionally, though lossy methods such as JPEG may provide acceptable compression for varying contone or grayscale image data, lossy methods tend not to work well on binary image data comprising sharp edges or transitions, thus also creating noise. In particular, JPEG compression tends to produce noise around edges of lines and text in image objects. To reduce the noise for image data compressed in JPEG format, one current solution is to reduce the amount of compression of the image data. However, reducing the amount of compression typically produces an undesirable result of a larger file size.
An alternative technique for manipulating digital image data includes segmentation (or auto-segmentation). Generally, auto-segmentation techniques are known, and are used to select the most appropriate method to render the various object types (e.g., black and white or color images, text, photos, etc.) present in an image. In some segmentation techniques, separation modules are used to detect and separate text from like halftone parts of image data so that text image data may compressed differently as compared to halftone image data. For example, it is generally known in the art that a format such as mixed raster content (MRC) (also referred to as multiple raster content) may be used to manipulate image data. However, such segmentation may be quite complex, as the types of data or objects in the image data must be determined, then separated or divided into segments or planes, and compressed according to the appropriate technique or method.
Therefore, a simpler, yet effective method for compressing and storing image data using segmentation techniques such as MRC, while still reducing noise and the like in the digitally output image, is desirable.